Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
LBP Discriminant Analysis for Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Feature Fusion of Face and Gait for Human Recognition at a Distance in Video
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
A probabilistic fusion methodology for face recognition
EURASIP Journal on Applied Signal Processing
A new method of feature fusion and its application in image recognition
Pattern Recognition
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Using random subspace to combine multiple features for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A novel feature fusion method based on partial least squares regression
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Audiovisual Synchronization and Fusion Using Canonical Correlation Analysis
IEEE Transactions on Multimedia
Image Classification Using Correlation Tensor Analysis
IEEE Transactions on Image Processing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Face recognition using dual-tree complex wavelet features
IEEE Transactions on Image Processing
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
Learning gabor features for facial age estimation
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Spectral Regression dimension reduction for multiple features facial image retrieval
International Journal of Biometrics
Weak metric learning for feature fusion towards perception-inspired object recognition
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Multimodal information spaces for content-based image retrieval
FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
Early and Late Fusion Methods for the Automatic Creation of Twitter Lists
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
Multi-resolution feature fusion for face recognition
Pattern Recognition
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Since the emergence of extensive multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and annotation. Existing feature fusion techniques simply concatenate a pair of different features or use canonical correlation analysis based methods for joint dimensionality reduction in the feature space. However, how to fuse multiple features in a generalized way is still an open problem. In this paper, we reformulate the multiple feature fusion as a general subspace learning problem. The objective of the framework is to find a general linear subspace in which the cumulative pairwise canonical correlation between every pair of feature sets is maximized after the dimension normalization and subspace projection. The learned subspace couples dimensionality reduction and feature fusion together, which can be applied to both unsupervised and supervised learning cases. In the supervised case, the pairwise canonical correlations of feature sets within the same classes are also counted in the objective function for maximization. To better model the high-order feature structure and overcome the computational difficulty, the features extracted from the same pattern source are represented by a single 2D tensor. The tensor-based dimensionality reduction methods are used to further extract low-dimensional discriminative features from the fused feature ensemble. Extensive experiments on visual data classification demonstrate the effectiveness and robustness of the proposed methods.